Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Artificial intelligence in the field of economics
TL;DR: This article explored the diffusion of AI and different AI methods (e.g., machine learning, deep learning, neural networks, expert systems, knowledge-based systems) through and within economic subfields, taking a scientometrics approach.
Journal ArticleDOI
Machine Learning-Based Relay Selection for Secure Transmission in Multi-Hop DF Relay Networks
TL;DR: Simulation results show that the proposed relay selection method can achieve near-optimal performance for an exhaustive search method for all combinations of relay selection, while computation time are reduced significantly.
Journal ArticleDOI
Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning
TL;DR: This study aims to develop and evaluate models of rental market dynamics using deep learning approaches on spatial and textual data from Craigslist rental listings and tests a number of machine learning and deep learning models for the prediction of rental prices based on data collected from Atlanta, GA, USA.
Book ChapterDOI
Supervised Learning for the Prediction of Firm Dynamics
TL;DR: This chapter illustrates a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle, and describes how SL tools can be used to analyze company growth and performance.
ReportDOI
Predicting Consumer Default: A Deep Learning Approach
TL;DR: This paper developed a model to predict consumer default based on deep learning and showed that the model consistently outperforms standard credit scoring models, even though it uses the same data set, and argued that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders.
References
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Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article
On Model Selection Consistency of Lasso
Peng Zhao,Bin Yu +1 more
TL;DR: It is proved that a single condition, which is called the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large.
Journal ArticleDOI
Clinical versus actuarial judgment
TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers